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This is a reconstruction of the SIMM model presented in Chapter 2 of Feedback Economics (Contemporary Systems Thinking)

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Implementation of a DSGE Model solved in a Macroeconomics class by Harald Uhlig (link), using Rational Expectations, in this case, the Hansens Real Business Cycle Model.

It shows the capacity of implementing Dynamic Stochastic General Equilibrium Model Analysis using System Dynamics.

Real Business Cycle Model (DSGE)

Fernando Fernandes Neto

Tesla pestel Analysis

Tesla pestel

Greg Dubrich

Simple mock-up model of how prioritizing various push-pull factors impacts the size of the immigrant population over time as well as economic benefits to the U.S. economy.

Immigrant Populations and Policy Implications

Kelsi Caywood

Socio-economic Cycle - Final Porjecy

Jeremy Tardioli

BACKGROUND:

The following simulation model demonstrates the relationship between supply, demand and pricing within the real estate and housing world. I have based the model on a small city with a population of 100,000 residents as of 2015.

AXIS:

X-Axis

The X-Axis shows the time. It begins in 2015 in the month of October and continues for 36 consecutive years.

Y-Axis

There are 2 Y-Axis on this model. The left hand side relates to the price, demand, and supply, while the right hand side solely lists the population.

As you could see, this town has a population of 100,000 residents to-date. The bottom of the model shows a population loop that produces an exponential growth rate of 2.5%. This dynamic and growing city populates approximately 240,000 residents after 36 years.

MODEL

The model consists of 2 folders named: Buyers/Consumers & Suppliers/Producers. This first folder represents the 'Demand'. It includes a buyers growth rate, buyers interest increase and decrease, a price demand and the demand price. The formulas form an exponential rise in demand due to the rapid and continuous increase in population in this new city. As population increases, so does the demand from buyers.

The second folder conveys the supply of houses. It includes a sophisticated loop of real estate. Residents who own houses in the market decide to sell the home. This becomes the Houses for sale, also known as the 'supply'. Those houses are sold and the sold houses re-enter the market and the loop continues.

The supply has an inverse relationship with the price. When prices drop, supplies drop because the demand goes up. And when the price goes up, so does the supply. This will represent the growth of new houses in the market.

PRICE

Note: The price is based on monthly rent rates.

The price is dependant on many variables. Most importantly, the supply and demand. It also includes factors such as expectations & the economic value of the house. I have included a stable, 'good' economic value for all homes as this fictional town is in a stable and growing area.

Price fluctuates throughout the entire simulation, however it also goes up in price. Over the years houses continue to rise in price while they regularly fluctuate. For example, in 2018 (3 years later), the max price for a home was: $4254.7 and min price was: $852.98. On the other hand, in October 2051 (36 years later), the max price was: $14906 and the min price was: $7661. (This is based on the following data: Houses for Sale: 500, Houses that have sold: 100, Houses in the Market: 730).

SLIDERS

There are 3 sliders on the bottom that could be altered. The simulation would react accordingly. The 3 sliders include changeable data on:

- Houses for Sale.

- Houses that have Sold.

- Houses in the Market.

Real Estate Simulation Assignment - Mitchell Bassil 43290264

Mitchell Bassil

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Introduction:

This model demonstrates the COVID-19 outbreak in Burnie, Tasmania. It shows how the government policy tries to reduce the spread of COVID-19 whilst also impacting the local economy.

Assumptions:

This model has four variables that influence the number of COVID-19 cases: infection rate, immunity loss rate, recovery rate and death rate.

In order to reduce the pandemic spread, in this model, assume the government released six policies when Burnie COVID-19 cases are equal or over 10 cases. Policies are vaccination promotion, travel restriction to Burnie, quarantine, social distance, lockdown and testing rate.

Government policies would reduce the pandemic. However, it decreases economic growth at the same time. In this model, only list three variable that influence local economic activities.

Travel restrictions and quarantine will reduce Burnie tourism and decrease the local economy. On the other hand, quarantine, social distance, lockdown allow people to stay at home, increasing E-commerce business.

As a result, policies that cause fewer COVID-19 cases also cause more considerable negative damage to the economy.

Interesting insights:

One of the interesting findings is that the government policy would reduce the COVID-19 spread significantly if I adjust the total government policies are over 20% (vaccine promotion, travel restriction, quarantine, social distance, lockdown), 3560 people will die, then no more people get COVID-19.

However, if I change the total government policy to less than 5%, the whole Burnie people will die according to the model. Therefore, we need to follow the polices, which saves our lives.

BMA708 assignment3 - Model of COVID-19 outbreak in Burnie

Zhiyuan Wang

A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy. Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover

Assumptions

Govt policy reduces infection and economic growth in the same way.

Govt policy is trigger when reported COVID-19 case are 10 or less.

A greater number of COVID-19 cases has a negative effect on the economy. This is due to economic signalling that all is not well.

Interesting insights

Higher testing rates trigger more rapid government intervention, which reduces infectious cases. The impact on the economy, though, of higher detected cases is negative.

Burnie COVID-19 outbreak demo model version 2

Steven D'Alessandro

39 4 months ago

This model simulates the economics of buying a home. It was created to compare buying a home against using investment returns to pay for rent. According to Micheal Finke, house prices typically run 20x monthly rental rates.

Try cloning this insight, setting the parameter values for real-world scenarios, and then running sensitivity analysis (see tools) to determine the likely wealth outcomes. Compare buying a home to renting. Note that each run will keep the parameters the same while simulating market volatility.

version 1.9

Home buying simulation 1.9

Larry Lee

4 2 months ago

This model demonstrate how the exisitng tested COVID cases effects economic recovery via goverment intervenes.

Assumption:Goverment intervenes positively contribute on transmission, patients recovery, and death elimination. When existing cases equal or lower than 10 cases, economic growth will be soaring with helps of influencial elements.

Interesting points: even though there are certain amount of unknow cases, enhancing social restriction and increasing test rate ould still reduce amount of cases

Complex Model to Simulate How COVID Outbreak Influence Economic Recovery in Burnie

Youjia Xiao

WIP Elements from macroeconomics, neoliberalism and commercial determinants of health frameworks to provide a background to the effects of the universal basic income on health and wellbeing for the first 1000 days. UBI diagram modified from Johnson2021 article Expanded in Insight 2

Employment and Welfare Interventions Effect on the first 1000 days 1

Geoff McDonnell

This Model was developed from the SEIR Model (Susceptible, Exposed, Infected, Recovered) and it predicts the COVID-19 outbreak in Burnie, Tasmania. This pandemic outbreak contributes to diverse rates including infection rate, death rates and recovery rate, government policies and its economic impacts.

Assumptions:

This model is driven by its determined rates, e.g., incubation rate, morality rate, test rate and immunity loss rate and its recovery rate.

Government policies are involved in fully vaccination rate, social distance, national border closure, travel, and business restriction which effect Burnie’s economy.

There are three economic entities dimensions in Burnie Island, we can tell that the pandemic has negative impact on Brick-and-Mortar enterprises and tourism business to some extent, whereas, e commercial business plays a crucial role to stimulate the regional economic activities during the COVID-19 period.

Interesting Insights:

The figure of susceptible changes significantly during the initial 3 weeks because of low recovery rate and high infection rate. On the other hand, the implementation and interventions of government policies is effective, because the number of patients who tested negative is increased and the majority of them release and go back home after medical follow-up.

Xueli Huang 501514, BMA708 Model of COVID-19 Outbreak in Burnie, Tasmania

XUELI HUANG

Based on the Market and Price simulation model in System Zoo 3.

I wrote an explanation of the model which you can find here: https://docs.google.com/document/d/1yRTtZvOOrFiBlK6pkvbpSUv_ajvGMKSAbfthRTBPU-8/edit?usp=sharing

Z504 Market and Price - System Zoo 3

Henny van Dongen

8

Based on the Market and Price simulation model in System Zoo 3, Z504. I made some more intrusive changes that make the model more realistic, or more 'economic', in another version 'simplified and improved'.

Simplified Z504 Market and Price - System Zoo 3

Henny van Dongen

5

This model simulates the economics of buying a home. It was created to compare buying a home against using investment returns to pay for rent.

Try cloning this insight, setting the parameter values for real-world scenarios, and then running sensitivity analysis (see tools) to determine the likely wealth outcomes. Compare buying a home to renting. Note that each run will keep the parameters the same while simulating market volatility.

version 1.8

Home buying simulation 1.8

Larry Lee

5

ISCI 360 Project - Stage 2

Our model examines the relationship between two straw types (plastic straws and biodegradable straws) and their impact on the environment and economics. Specifically, we are interested in figuring out whether biodegradable straws are a viable solution to plastic straws.

Our model is broken down into three aspects: Social, Environmental and Economic. Color coding is used to differentiate between the different aspects and is explained below:

Turquoise represents the social aspect.

Purple represents the economic aspects.

Green represents the environmental aspects.

Blue represents other crucial stocks and flows in the model that do not necessarily fit into the three aspects above.

In our model, the Canadian population is assumed to increase steadily until a carrying capacity is reached. This can be seen in the graph as the line increases linearly before plateauing indefinitely. We assumed that we will be able to maintain the population at our carrying capacity due to technological advances.

Social Aspect:

The social aspect refers to the impact that awareness of the detrimental costs of straws can have on the usage of straws. The two flows that contribute to awareness are word of mouth (i.e. your friends and family informing you about the effects of straws and influencing you to stop using them) and media coverage (i.e. the media highlights the effects of straws). Both of these flows are dependent on the Canadian population such that 25% of the Canadian population at any time will be impacted by word of mouth or media coverage. (Side note: since word of mouth and media coverage are dependent on the Canadian population, they will plateau when the population does.) This is an arbitrary number but was chosen to show what a change in perspectives of the Canadian population can do. These flows input into an 'awareness of detrimental effects of using plastic straws' stock that reduces the number of plastic straws being used.

Plastic Straws

According to data from the United States individuals usually use 1.6 straws everyday and thus, we have assumed that to be true in Canada as well. Plastic straws start at a base value (due to the previous straw usage) and grow with the Canadian population while subtracting the awareness component of the model.

Environmental Aspect

Since the decomposition of plastic versus paper is significantly different, the amounts that accumulate in the ocean and landfills can be monitored. In addition, the impact on the environment can be monitored. Since plastic straws take longer to decompose, they have a larger impact on wildlife in the ocean than biodegradable straws. Thus, as the plastic straw usage decreases, the amount of habitat loss occurring plateaus. We have also included the aspect of clean-up in which the plastic from the ocean can be moved to the landfill. You will notice that the habitat loss plateaus but does not decrease. This is because we cannot reverse the damage we have done (without additional rigorous clean-up) but can mitigate additional damage. (Please note that clean-up affects only the stock 'Plastic Straws in the ocean' and thus, does not affect the stock 'habitat loss.' Therefore, clean-up will reduce the number of plastic straws in the ocean and indirectly affect the stock 'habitat loss.' However, it will not clean up the plastic straws already impacting 'habitat loss.')

Economic Aspect

The economic aspect monitors the amount of money it takes to make plastic straws versus biodegradable straws and the amount of money the government needs to fund ocean clean-ups. It can be seen that a the usage of plastic straws decreases, the need for clean-up money from the government decreases. However, there is a base level of damage that has already been done by us and thus, larger scale clean-ups will be needed to reverse that. In other words, smaller clean-ups will mitigate the damage we are currently doing but not reverse the damage we have already done. We can also track the cost of making each straw; it can be seen that biodegradable straws are more expensive to make.

However, the energy required to make the straws is less for biodegradable straws than plastic straws. Thus, there are trade-offs for using biodegradable straws.

Although, biodegradable straws are more expensive, they require less energy to make, decompose faster, require less funding for clean-up and impact the wildlife in the ocean to a lesser degree.

Project Stage 2

Eisha Sharda

There are many reasons why reality does not alter doctrines. Some of the factors and their dynamics are shown in the CLD.

However, an unchanging doctrine may prompt actions that influence and change reality. Do ill-adapted doctrinal reactions not increase the complexity in the world, potentially making everything worse? Some Neoliberal economic remedies come to mind.

THE INALTERABILITY OF DOCTRINES TENDS TO INCREASE COMPLEXITY

Hanns-Jürgen Hodann

Book Summary of The Great Transformation by Karl Polanyi see Wikipedia . See also more Karl Polanyi ideas IM-181325

The Great Transformation

Geoff McDonnell

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First pass at model depicting importance of Net Capital Accumulation on economic growth of firm - from firm's perspective

Economic Growth Rev 0

Gary Midock

Economic Model

Maria Sol Perez Aguirreburualde

THE BROKEN LINK BETWEEN SUPPLY AND DEMAND CREATES TURBULENT CHAOTIC DESTRUCTION

The existing global capitalistic growth paradigm is totally flawed

Growth in supply and productivity is a summation of variables as is demand ... when the link between them is broken by catastrophic failure in a component the creation of unpredictable chaotic turbulence puts the controls ito a situation that will never return the system to its initial conditions as it is STIC system (Lorenz)

The chaotic turbulence is the result of the concept of infinite bigness this has been the destructive influence on all empires and now shown up by Feigenbaum numbers and Dunbar numbers for neural netwoirks

See Guy Lakeman Bubble Theory for more details on keeping systems within finite working containers (villages communities)

THE BROKEN LINK BETWEEN SUPPLY AND DEMAND CREATES CHAOTIC TURBULENCE (+controls)